from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""pixel_values"""]







   def __init__(     self						:						int   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						PILImageResampling = PILImageResampling.BICUBIC   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Union[int, float] = 1 / 255   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN   ,   lowerCamelCase_						:						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD   ,   **lowerCamelCase_						:						List[str]   ,   ):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      size if size is not None else {"""shortest_edge""": 224}
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   default_to_square=lowerCamelCase_				)
    UpperCamelCase			  =      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   param_name="""crop_size"""				)

    UpperCamelCase			  =      do_resize
    UpperCamelCase			  =      size
    UpperCamelCase			  =      resample
    UpperCamelCase			  =      do_center_crop
    UpperCamelCase			  =      crop_size
    UpperCamelCase			  =      do_rescale
    UpperCamelCase			  =      rescale_factor
    UpperCamelCase			  =      do_normalize
    UpperCamelCase			  =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
    UpperCamelCase			  =      image_std if image_std is not None else IMAGENET_DEFAULT_STD







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Dict[str, int]   ,   lowerCamelCase_						:						PILImageResampling = PILImageResampling.BICUBIC   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						Union[str, Any]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   default_to_square=lowerCamelCase_				)
    # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
    if "shortest_edge" in size:
     UpperCamelCase			  =      int((256 / 224) * size["""shortest_edge"""]				)
     UpperCamelCase			  =      get_resize_output_image_size(lowerCamelCase_   ,   size=lowerCamelCase_   ,   default_to_square=lowerCamelCase_				)
     UpperCamelCase			  =      {"""height""": output_size[0], """width""": output_size[1]}
    if "height" not in size_dict or "width" not in size_dict:
     raise ValueError(
         f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""				)
    return resize(
        lowerCamelCase_   ,   size=(size_dict["""height"""], size_dict["""width"""])   ,   resample=lowerCamelCase_   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Dict[str, int]   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						List[Any]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      get_size_dict(lowerCamelCase_				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""				)
    return center_crop(lowerCamelCase_   ,   size=(size["""height"""], size["""width"""])   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Union[int, float]   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						Dict   ,   ):





    """simple docstring"""


    return rescale(lowerCamelCase_   ,   scale=lowerCamelCase_   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Union[float, List[float]]   ,   lowerCamelCase_						:						Union[float, List[float]]   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						int   ,   ):





    """simple docstring"""


    return normalize(lowerCamelCase_   ,   mean=lowerCamelCase_   ,   std=lowerCamelCase_   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						ImageInput   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[Dict[str, int]] = None   ,   lowerCamelCase_						:						PILImageResampling = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[Dict[str, int]] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[float] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[Union[float, Iterable[float]]] = None   ,   lowerCamelCase_						:						Optional[Union[float, Iterable[float]]] = None   ,   lowerCamelCase_						:						Optional[TensorType] = None   ,   lowerCamelCase_						:						ChannelDimension = ChannelDimension.FIRST   ,   **lowerCamelCase_						:						Optional[Any]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      do_resize if do_resize is not None else self.do_resize
    UpperCamelCase			  =      resample if resample is not None else self.resample
    UpperCamelCase			  =      do_center_crop if do_center_crop is not None else self.do_center_crop
    UpperCamelCase			  =      do_rescale if do_rescale is not None else self.do_rescale
    UpperCamelCase			  =      rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCamelCase			  =      do_normalize if do_normalize is not None else self.do_normalize
    UpperCamelCase			  =      image_mean if image_mean is not None else self.image_mean
    UpperCamelCase			  =      image_std if image_std is not None else self.image_std

    UpperCamelCase			  =      size if size is not None else self.size
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   default_to_square=lowerCamelCase_				)
    UpperCamelCase			  =      crop_size if crop_size is not None else self.crop_size
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   param_name="""crop_size"""				)

    UpperCamelCase			  =      make_list_of_images(lowerCamelCase_				)

    if not valid_images(lowerCamelCase_				):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""				)

    if do_resize and size is None:
     raise ValueError("""Size must be specified if do_resize is True."""				)

    if do_center_crop and crop_size is None:
     raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

    if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

    if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

    # All transformations expect numpy arrays.
    UpperCamelCase			  =      [to_numpy_array(lowerCamelCase_				) for image in images]

    if do_resize:
     UpperCamelCase			  =      [self.resize(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				) for image in images]

    if do_center_crop:
     UpperCamelCase			  =      [self.center_crop(lowerCamelCase_   ,   lowerCamelCase_				) for image in images]

    if do_rescale:
     UpperCamelCase			  =      [self.rescale(lowerCamelCase_   ,   lowerCamelCase_				) for image in images]

    if do_normalize:
     UpperCamelCase			  =      [self.normalize(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				) for image in images]

    UpperCamelCase			  =      [to_channel_dimension_format(lowerCamelCase_   ,   lowerCamelCase_				) for image in images]

    UpperCamelCase			  =      {"""pixel_values""": images}
    return BatchFeature(data=lowerCamelCase_   ,   tensor_type=lowerCamelCase_				)

_SCREAMING_SNAKE_CASE 		=      9.8_06_65





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_ = g       )		->	float:







 '''simple docstring'''

 if fluid_density <= 0:
  raise ValueError("""Impossible fluid density"""       )
 if volume < 0:
  raise ValueError("""Impossible Object volume"""       )
 if gravity <= 0:
  raise ValueError("""Impossible Gravity"""       )

 return fluid_density * gravity * volume


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()
